As noted previously, there are a huge number of resources on how to get started with R, available online. I will be making my own contributions to this genre but before I really get rolling with that, it is fair to list the ‘Getting started with R’ resources that other people have listed previously.

(In my experience, learning about something from a number of different angles can be very helpful.)

Getting started is best done through having an analysis you need to do, some data you will analyze, and a bit of time. There is no point reading the guides listed following without some data of your own that you want to make sense of. If you do not already have some data to analyse, I shall be providing some in following posts.

How I got started

I did research and published a couple of papers using R to do analysis with TINN-R as a text editor for writing the analysis scripts.

— I learnt a tremendous amount working through a combination of Harald Baayen’s book:

— NB that Kabacoff (In the Quick-R website) explains the perception of a ‘steep learning curve’ for R: no single tutorial will (or can) cover everything; you interact with R, you don’t use press a button and get the product.

I would install R, and R-Studio, to which I shall return, read in some of my data (maybe, preferably some data I had already analysed, say, in SPSS) and work through the tests I would want to do. My problem – when getting started – was that I needed to do mixed-effects modelling and, ultimately, because mixed-effects modelling is, essentially, *the* analysis approach now in psycholinguistics, learning R to solve that problem drove me to a place I’m now happy to reside in, permanently; I suppose you might come to R for a holiday (the graphs are nice) or a short-term stay (it’s sunnier back in SPSS-land).

— A basic, immediate, reason, is that it provides a helpfully laid out editor.

— The in-my-future reasons include:

1. I want to be able to do my analysis and write my papers in the one editor, which R-Studio is designed to allow me to do: reproducible research using knitr, see here, here, here, here, here and here.

2. I am involved in many different projects, involving differing analyses, and I want to be able to control the flow of information efficiently: version control using git, see here.

I would then try to ground my understanding in the language, starting with the Try-R tutorial and moving on from there, using excellent books like:

— Working through tutorials e.g. those listed here by Pairach Piboonrungroj: I am kind of disappointed he stopped short of 100 [joking].

— and maybe going online to learn at venues like Coursera, in courses such as those presented by Jeff Leek, Drew Conway, and Roger Peng.

— NB I like the R Cookbook because it takes a format – have a need, find a recipe to fill that need – that is useful. I like R in Action because it is clear, concise, practical and pitched at a good level.

I would look around the R-Bloggers aggregation of R blog posts for stuff that interests me e.g. here.

Of course, there are free books available online, accessible at the CRAN website, also here.

I use a bunch of the functions furnished in the psych package by William Revelle, and unsurprisingly the getting-started guide is helpful and clear; see the short and very short guides here along with a helpful set of notes.

This tutorial will take you from start (install packages) to finish (SEM etc.) in a blog post.

Alastair Sanderson’s R blog is astronomy focused but the introduction, while sparsely explained (e.g. do you know what a vector is? it won’t explain – but then, if you’re an astronomer you would already know) is clear and comprehensively informative.

Lists of resources including tutorials

This post by Jeromy Anglin both provides some good advice and lists some very helpful resources, with a focus on psychology: in fact, check out the rest of his website; and his list of video tutorials.